Past Causalities and Event Categories for Connecting Similar Past and Present Causalities

138 past causalities and 900 Wikipedia events annotated with 13 categories for connecting historical and present social issues. The dataset was created by researchers at The University of Tokyo by analyzing Japanese high-school textbooks and an encyclopedia of historiography. It was used to train an SVM-RBF model achieving 63.5% F1 score.

Use Cases

• Training text classification models to categorize historical events based on the provided 13 categories.
• Analyzing analogies between past and present social issues using the structured causality descriptions (background, solution, result).
• Enhancing educational materials by linking historical causalities from textbooks to contemporary news topics.

Strengths

• Includes 138 manually selected past causalities with structured text descriptions.
• Provides 900 additional Wikipedia event entries for model training.
• Reports specific model performance metrics (73.6% precision, 55.8% recall, 63.5% F1 score).

Limitations

• Row count for the main causality and event files is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic and educational bias inherent to its source in Japanese high-school textbooks.

Provenance

Source

Japanese high-school textbooks and Wikipedia.

Collection Method

Causalities were collected from textbooks, selected for relevance to present social issues, and categorized using an encyclopedia of historiography.